3 The manufacturer of Beanie Baby dolls used quarterly price data for 2005 /-2013 /V(-1,..., 36) and the regression equationPta+ bt+c₁ D1 + c2 021+ c3 D3 tto forecast doll prices in the year 2014. Pt is the quarterly price of dolls, and D1, D2, and D3 are dummy variables for quarters I, I, and II, respectively.DEPENDENT VARIABLE: PTOBSERVATIONS:R-SQUARE0.9078P-VALUE ON F0.000136PARAMETERF-RATIO76.34STANDARDERROR6.20VARIABLEESTIMATEP-VALUET-RATIO3.87INTERCEPT24.00.0005T0.8000.2403.330.0022-8.0D12.60-3.080.0043-6.001.80-3.33D20.00220.60-40-6.67D30.0001quarter is significantly higher than in any other quarter.At the 2 percent level of statistical significance, the estimation results indicate that price in theO 1stO 2ndO 3rdO 4thActivate WindowGo to Settings to activa

Statisticians use the time-series regression analysis to predict future values.

Answered: The manufacturer of Beanie Baby dolls…

College Algebra

10th Edition

ISBN:9781337282291

Author:Ron Larson

Publisher:Ron Larson

Chapter3: Polynomial Functions

Section3.5: Mathematical Modeling And Variation

Problem 2ECP

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
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The manufacturer of Beanie Baby dolls used quarterly price data for 2012/- 2020/V (t = 1, ..., 36) and the regression equation Pt = a + bt + c₁D1 t + c2 D2t + c3D3 t to forecast doll prices in the year 2021. Pt is the quarterly price of dolls, and D1, D2t, and D3 are dummy variables for quarters I, II, and III, respectively. DEPENDENT VARIABLE: PT R-SQUARE P-VALUE ON F 0.0001 OBSERVATIONS: 36 0.9078 F-RATIO 76.34 STANDARD ERROR PARAMETER ESTIMATE T-RATIOP-VALUE VARIABLE INTERCEPT 24.0 6.20 3.87 0.0005 T 0.8 0.240 3.33 0.0022 D1 -8.0 2.60 -3.08 0.0043 D2 -6.0 1.80 -3.33 0.0022 D3 -4.0 0.60 -6.67 0.0001 In any given year price tends to vary from quarter to quarter as follows: Multiple Choice O O O P₁ > P|| > P||| > PIV PI> PIV> Pill > PII Pll > Pill > PIV> PI Pill > PI>PII > PIV PIV> Pill > PII > PI
The manufacturer of Beanie Baby dolls used quarterly price data for 2012/-2020/V (t = 1, ..., 36) and the regression equation Pt= a + bt+c₁D1 t + c2 D2 + + c3 D3 t to forecast doll prices in the year 2021. Pt is the quarterly price of dolls, and D1, D2t, and D3+ are dummy variables for quarters I, II, and III, respectively. DEPENDENT VARIABLE: PT R-SQUARE P-VALUE ON F 0.0001 OBSERVATIONS: F-RATIO 76.34 STANDARD 36 0.9078 PARAMETER VARIABLE ESTIMATE ERROR T-RATIOP-VALUE INTERCEPT 24.0 6.20 3.87 0.0005 T 0.8 0.240 3.33 0.0022 D1 -8.0 2.60 -3.08 0.0043 D2 -6.0 1.80 -3.33 0.0022 D3 -4.0 0.60 -6.67 0.0001 The estimated quarterly increase in price is and the estimated annual increase in price is Multiple Choice O O $1.50; $6.00 $1.40; $4.00 $0.60; $2.40 $0.80; $3.20 None of the choices are correct.
The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Interpret the slope.
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The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Predict the median income of a region in which 30% of adults 25 years and older have at least a bachelor's degree.
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The least-squares regression equation is y = 602.1x + 17,023 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.6748. Complete parts (a) through (d). C Median Income 55000- 25000 ● (c) Interpret the slope. Select the correct choice below and fill in the answer box to complete your choice. (Type an integer or decimal. Do not round.) A. For 0% of adults having a bachelor's degree, the median income is predicted to be $ B. For a median income of $0, the percent of adults with a bachelor's degree is %. O C. For every dollar increase in median income, the percent of adults having at least a bachelor's degree is %, on average. OD. For every percent increase in adults having at least a bachelor's degree, the median income increases by $, on average. 15 20 25 30 35 40 45 50 55 60 Bachelor's % U (a) Predict the…

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